The Team
Categorica is a small group of mathematicians and engineers who bonded over a shared love of writing reliable, performant and easily extensible software. We want to build an engineering focused company that can do this repeatably as we believe that longterm, competitive advantage depends upon the quality of the product and the speed with which one can innovate to improve that; achieving these is made possible by this approach. Also, daily quality of life is much better which we think (with less evidence) makes us more productive.
We contend that, to deliver a good product, you need to have solid foundations which are maintained and evolved: what works on day one isn’t what you want a year later and even less two years later. Building quality core components leads to speed of innovation being maintained at all levels, from onboarding feeds to building models and complex calculations on them.
We want to build a company that remains perpetually innovative and inquisitive, that seeks to do better today than we did yesterday and aspires to do better tomorrow than today.
Categorica is our attempt at this ideal.
Paul
Following a masters in engineering and a doctorate in medical imaging at Oxford, Paul joined Toshiba as a research fellow in their Tokyo laboratories working on machine learning. After several years of research on feature extraction, object recognition and tracking he transitioned to Finance, working as a Quant Dev and then Quant for RBS in Tokyo. Over the next twenty years he gained experience building large scale distributed risk and pricing systems for fixed income, credit and FX in Asia and the UK. He is particularly interested in systems control theory and modelling of structured data.
Dan
After an undergraduate and masters in computer science at the University of Bristol, Dan initially worked in academia on high performance computing. After leaving academia for industry, he found an interest in financial software stacks, specifically in lower latency & performance sensitive fields.
Séamus
After an undergraduate degree in maths at Trinity College Dublin and Part III at Cambridge, focused on probability theory, Séamus entered finance to pursue its application. Over the next ten years he worked across multiple asset classes in both traditional quantitative finance and electronic trading, finding both the infrastructure and the modelling interesting. Following this, he moved to Fintech for the opportunity to experiment with new ideas. He is particularly interested in systems that make computations reproducible and enforce correctness.
Jakob
Jakob obtained his research master in computer science from MPRI and a doctorate in category theory and type theory at Leeds University. After academia, he joined a London-based fintech startup specialising in financial data management, as part of the research and analytics team. He has also worked on low latency market data feeds at LSEG.
